Incidence and prediction of cutaneous leishmaniasis cases and its related factors in an endemic area of Southeast Morocco: time series analysis
Adnane Hakem,
No information about this author
Abdelaati El Khiat,
No information about this author
Abdelkacem Ezzahidi
No information about this author
et al.
Acta Tropica,
Journal Year:
2025,
Volume and Issue:
unknown, P. 107579 - 107579
Published: March 1, 2025
Language: Английский
Landslide Susceptibility Assessment Using Recurrent Neural Network (RNN)—A Case of Chabahar and Konarak in Iran
Indian geotechnical journal,
Journal Year:
2025,
Volume and Issue:
unknown
Published: April 3, 2025
Language: Английский
Antimalarial and other antiprotozoal products from African medicinal plants
Elsevier eBooks,
Journal Year:
2025,
Volume and Issue:
unknown, P. 643 - 659
Published: Jan. 1, 2025
Language: Английский
Artificial Intelligence in Cutaneous Leishmaniasis Diagnosis: Current Developments and Future Perspectives
Hasnaa Talimi,
No information about this author
Kawtar Retmi,
No information about this author
Rachida Fissoune
No information about this author
et al.
Diagnostics,
Journal Year:
2024,
Volume and Issue:
14(9), P. 963 - 963
Published: May 5, 2024
Cutaneous
Leishmaniasis
(CL)
is
a
major
global
health
problem
requiring
appropriate
diagnosis
methods.
Its
challenging,
particularly
in
resource-limited
settings.
The
integration
of
Artificial
Intelligence
(AI)
into
medical
diagnostics
has
shown
promising
results
various
fields,
including
dermatology.
In
this
systematic
review,
we
aim
to
highlight
the
value
using
AI
for
CL
and
AI-based
algorithms
that
are
employed
process,
identify
gaps
need
be
addressed.
Our
work
highlights
only
limited
number
studies
related
diagnosis.
Among
these
studies,
seven
were
identified
future
research.
Addressing
considerations
will
pave
way
development
robust
systems
encourage
more
research
detection
by
AI.
This
could
contribute
improving
and,
ultimately,
healthcare
outcomes
CL-endemic
regions.
Language: Английский
Diversity of fruit fly species in agricultural area Chott Zahrez Chergui (Djel-fa): First record of Trupanea amoena (Diptera: Tephritidae) in Algeria
Siham Rekia Yahiaoui,
No information about this author
Boudjema Sehl,
No information about this author
Faïza Marniche
No information about this author
et al.
Journal of Agriculture and Applied Biology,
Journal Year:
2024,
Volume and Issue:
5(1), P. 48 - 62
Published: April 1, 2024
Fruit
flies
(Diptera:
Tephritidae)
are
cosmopolitan,
species-rich,
and
yet
poorly
studied
in
Algeria,
particularly
the
agro
area
of
Djelfa.
We
sampled
fruit
orchard
containing
three
types
trees
apricot,
fig
grapes
at
each
site,
using
yellow
traps.
Results
show
that
differed
significantly
species
richness,
abundance,
diversity
evenness.
A
total
956
individuals
distributed
by
nine
Diptera,
including
four
collected.
The
results
revealed
most
abundant
Drosophila
melanogaster
apricot
site
(RA%=28.32%),
Zaprionus
indianus
(RA%=34.74
%)
Tephritis
praecox
with
(RA%=
51.61
%).
selected
a
greater
number
individuals,
higher
indices
H′=2.40
bits;
2.32
bits
2.02
bits.
Statistically,
result
stepwise
multiple
regressions
very
strong
correlation
between
fly
temperatures
(r=93.1%)
(Sig
.000**).
Thus,
appear
to
have
rather
arrow
host
plant
requirements
their
phenology
was
correlated
environmental
differences.
Our
study
highlighted
for;
first
time;
presence
Trupanea
amoena
Algeria.
This
important
substantial
preliminary
work
on
this
new
Algeria
provides
solid
basis
for
future
research
extension
particular
monitoring
control
dreaded
agricultural
pest.
Language: Английский